Using satellite estimates of aboveground biomass to assess carbon stocks in a mixed-management, semi-deciduous tropical forest in the Yucatan Peninsula

Information on the spatial distribution of forest aboveground biomass (AGB) and its uncertainty is important to evaluate management and conservation policies in tropical forests. However, the scarcity of field data and robust protocols to propagate uncertainty prevent a robust estimation through remote sensing. We upscaled AGB from field data to LiDAR, and to landscape scale using Sentinel-2 and ALOS-PALSAR through machine learning, propagated uncertainty using a Monte Carlo framework and explored the relative contributions of each sensor. Sentinel-2 outperformed ALOS-PALSAR (R2 = 0.66, vs 0.50), however, the combination provided the best fit (R2 = 0.70). The combined model explained 49% of the variation comparing against plots within the calibration area, and 17% outside, however, 94% of observations outside calibration area fell within the 95% confidence intervals. Finally, we partitioned the distribution of AGB in different management and conservation categories for evaluating the potential of different strategies for conserving carbon stock

Isolating the effects of land use and functional variation on Yucatán's forest biomass under global change

Tropical forests hold large stocks of carbon in biomass and face pressures from changing climate and anthropogenic disturbance. Forests' capacity to store biomass under future conditions and accumulate biomass during regrowth after clearance are major knowledge gaps. Here we use chronosequence data, satellite observations and a C-cycle model to diagnose woody C dynamics in two dry forest ecotypes (semi-deciduous and semi-evergreen) in Yucatán, Mexico. Woody biomass differences between mature semi-deciduous (90 MgC ha−1) and semi-evergreen (175 MgC ha−1) forest landscapes are mostly explained by differences in climate (c. 60%), particularly temperature, humidity and soil moisture effects on production. Functional variation in foliar phenology, woody allocation, and wood turnover rate explained c. 40% of biomass differences between ecotypes. Modeling experiments explored varied forest clearance and regrowth cycles, under a range of climate and CO2 change scenarios to 2100. Production and steady state biomass in both ecotypes were reduced by forecast warming and drying (mean biomass 2021–2100 reduced 16–19% compared to 2001–2020), but compensated by fertilisation from rising CO2. Functional analysis indicates that trait adjustments amplify biomass losses by 70%. Experiments with disturbance and recovery across historically reported levels indicate reductions to mean forest biomass stocks over 2021–2100 similar in magnitude to climate impacts (10–19% reductions for disturbance with recovery). Forest disturbance without regrowth amplifies biomass loss by three- or four-fold. We conclude that vegetation functional differences across the Yucatán climate gradient have developed to limit climate risks. Climate change will therefore lead to functional adjustments for all forest types. These adjustments are likely to magnify biomass reductions caused directly by climate change over the coming century. However, the range of impacts of land use and land use change are as, or more, substantive than the totality of direct and indirect climate impacts. Thus the carbon storage of Yucatan's forests is highly vulnerable both to climate and land use and land use change. Our results here should be used to test and enhance land surface models use for dry forest carbon cycle assessment regionally and globally. A single plant functional type approach for modeling Yucatán's forests is not justified

Environmental gradients and the evolution of successional habitat specialization: A test case with 14 Neotropical forest sites

© 2015 British Ecological Society. Successional gradients are ubiquitous in nature, yet few studies have systematically examined the evolutionary origins of taxa that specialize at different successional stages. Here we quantify successional habitat specialization in Neotropical forest trees and evaluate its evolutionary lability along a precipitation gradient. Theoretically, successional habitat specialization should be more evolutionarily conserved in wet forests than in dry forests due to more extreme microenvironmental differentiation between early and late-successional stages in wet forest. We applied a robust multinomial classification model to samples of primary and secondary forest trees from 14 Neotropical lowland forest sites spanning a precipitation gradient from 788 to 4000 mm annual rainfall, identifying species that are old-growth specialists and secondary forest specialists in each site. We constructed phylogenies for the classified taxa at each site and for the entire set of classified taxa and tested whether successional habitat specialization is phylogenetically conserved. We further investigated differences in the functional traits of species specializing in secondary vs. old-growth forest along the precipitation gradient, expecting different trait associations with secondary forest specialists in wet vs. dry forests since water availability is more limiting in dry forests and light availability more limiting in wet forests. Successional habitat specialization is non-randomly distributed in the angiosperm phylogeny, with a tendency towards phylogenetic conservatism overall and a trend towards stronger conservatism in wet forests than in dry forests. However, the specialists come from all the major branches of the angiosperm phylogeny, and very few functional traits showed any consistent relationships with successional habitat specialization in either wet or dry forests. Synthesis. The niche conservatism evident in the habitat specialization of Neotropical trees suggests a role for radiation into different successional habitats in the evolution of species-rich genera, though the diversity of functional traits that lead to success in different successional habitats complicates analyses at the community scale. Examining the distribution of particular lineages with respect to successional gradients may provide more insight into the role of successional habitat specialization in the evolution of species-rich taxa

Environmental gradients and the evolution of successional habitat specialization: A test case with 14 Neotropical forest sites

https://www.scopus.com/inward/record.url?eid=2-s2.0-84939570316&partnerID=40&md5=fcadae8e6c274e8b7efca96099304a7cSuccessional gradients are ubiquitous in nature, yet few studies have systematically examined the evolutionary origins of taxa that specialize at different successional stages. Here we quantify successional habitat specialization in Neotropical forest trees and evaluate its evolutionary lability along a precipitation gradient. Theoretically, successional habitat specialization should be more evolutionarily conserved in wet forests than in dry forests due to more extreme microenvironmental differentiation between early and late-successional stages in wet forest. We applied a robust multinomial classification model to samples of primary and secondary forest trees from 14 Neotropical lowland forest sites spanning a precipitation gradient from 788 to 4000 mm annual rainfall, identifying species that are old-growth specialists and secondary forest specialists in each site. We constructed phylogenies for the classified taxa at each site and for the entire set of classified taxa and tested whether successional habitat specialization is phylogenetically conserved. We further investigated differences in the functional traits of species specializing in secondary vs. old-growth forest along the precipitation gradient, expecting different trait associations with secondary forest specialists in wet vs. dry forests since water availability is more limiting in dry forests and light availability more limiting in wet forests. Successional habitat specialization is non-randomly distributed in the angiosperm phylogeny, with a tendency towards phylogenetic conservatism overall and a trend towards stronger conservatism in wet forests than in dry forests. However, the specialists come from all the major branches of the angiosperm phylogeny, and very few functional traits showed any consistent relationships with successional habitat specialization in either wet or dry forests. Synthesis. The niche conservatism evident in the habitat specialization of Neotropical trees suggests a role for radiation into different successional habitats in the evolution of species-rich genera, though the diversity of functional traits that lead to success in different successional habitats complicates analyses at the community scale. Examining the distribution of particular lineages with respect to successional gradients may provide more insight into the role of successional habitat specialization in the evolution of species-rich taxa

Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics

Regrowth of tropical secondary forests following complete or nearly complete removal of forest vegetation actively stores carbon in aboveground biomass, partially counterbalancing carbon emissions from deforestation, forest degradation, burning of fossil fuels, and other anthropogenic sources. We estimate the age and spatial extent of lowland second-growth forests in the Latin American tropics and model their potential aboveground carbon accumulation over four decades. Our model shows that, in 2008, second-growth forests (1 to 60 years old) covered 2.4 million km2 of land (28.1%of the total study area).Over 40 years, these lands can potentially accumulate a total aboveground carbon stock of 8.48 Pg C (petagrams of carbon) in aboveground biomass via low-cost natural regeneration or assisted regeneration, corresponding to a total CO2 sequestration of 31.09 Pg CO2. This total is equivalent to carbon emissions from fossil fuel use and industrial processes in all of Latin America and the Caribbean from1993 to 2014. Ten countries account for 95% of this carbon storage potential, led by Brazil, Colombia, Mexico, and Venezuela. We model future land-use scenarios to guide national carbon mitigation policies. Permitting natural regeneration on 40% of lowland pastures potentially stores an additional 2.0 Pg C over 40 years. Our study provides information and maps to guide national-level forest-based carbon mitigation plans on the basis of estimated rates of natural regeneration and pasture abandonment. Coupled with avoided deforestation and sustainable forestmanagement, natural regeneration of second-growth forests provides a low-costmechanism that yields a high carbon sequestration potential with multiple benefits for biodiversity and ecosystem services. © 2016 The Authors

Biodiversity recovery of Neotropical secondary forests

Old-growth tropical forests harbor an immense diversity of tree species but are rapidly being cleared, while secondary forests that regrow on abandoned agricultural lands increase in extent. We assess how tree species richness and composition recover during secondary succession across gradients in environmental conditions and anthropogenic disturbance in an unprecedented multisite analysis for the Neotropics. Secondary forests recover remarkably fast in species richness but slowly in species composition. Secondary forests take a median time of five decades to recover the species richness of old-growth forest (80% recovery after 20 years) based on rarefaction analysis. Full recovery of species composition takes centuries (only 34% recovery after 20 years). A dual strategy that maintains both old-growth forests and species-rich secondary forests is therefore crucial for biodiversity conservation in human-modified tropical landscapes. Copyright © 2019 The Authors, some rights reserved

Effects of Sample Plot Size and GPS Location Errors on Aboveground Biomass Estimates from LiDAR in Tropical Dry Forests

Accurate estimates of above ground biomass (AGB) are needed for monitoring carbon in tropical forests. LiDAR data can provide precise AGB estimations because it can capture the horizontal and vertical structure of vegetation. However, the accuracy of AGB estimations from LiDAR is affected by a co-registration error between LiDAR data and field plots resulting in spatial discrepancies between LiDAR and field plot data. Here, we evaluated the impacts of plot location error and plot size on the accuracy of AGB estimations predicted from LiDAR data in two types of tropical dry forests in Yucatán, México. We sampled woody plants of three size classes in 29 nested plots (80 m2, 400 m2 and 1000 m2) in a semi-deciduous forest (Kiuic) and 28 plots in a semi-evergreen forest (FCP) and estimated AGB using local allometric equations. We calculated several LiDAR metrics from airborne data and used a Monte Carlo simulation approach to assess the influence of plot location errors (2 to 10 m) and plot size on ABG estimations from LiDAR using regression analysis. Our results showed that the precision of AGB estimations improved as plot size increased from 80 m2 to 1000 m2 (R2 = 0.33 to 0.75 and 0.23 to 0.67 for Kiuic and FCP respectively). We also found that increasing GPS location errors resulted in higher AGB estimation errors, especially in the smallest sample plots. In contrast, the largest plots showed consistently lower estimation errors that varied little with plot location error. We conclude that larger plots are less affected by co-registration error and vegetation conditions, highlighting the importance of selecting an appropriate plot size for field forest inventories used for estimating biomass

Impact of Urban Land-Cover Changes on the Spatial-Temporal Land Surface Temperature in a Tropical City of Mexico

Climate change has severe consequences on ecosystem processes, as well as on people’s quality of life. It has been suggested that the loss of vegetation cover increases the land surface temperature (LST) due to modifications in biogeochemical patterns, generating a phenomenon known as “urban heat island” (UHI). The aim of this work was to analyze the effects of urban land-cover changes on the spatiotemporal variation of surface temperature in the tropical city of Mérida, Mexico. To find these effects we used both detected land-cover changes as well as variations of the Normalized Difference Vegetation Index (NDVI). Mérida is ranked worldwide as one of the best cities to live due to its quality of life. Data from satellite images of Landsat were analyzed to calculate land use change (LUC), LST, and NDVI. LST increased ca. 4 °C in the dry season and 3 °C in the wet season because of the LUC. In addition, a positive relationship between the LST and the NDVI was observed mainly in the dry season. The results confirm an increase in the LST as a consequence of the loss of vegetation cover, which favors the urban heat island phenomenon

Patterns and correlates of plant diversity differ between common and rare species in a neotropical dry forest

Determining which factors affect species richness is important for conservation theory and practice. However, richness of common and rare species may be affected by different factors. We use an extensive inventory of woody plants from a tropical dry forest landscape in Yucatan, Mexico to assess the unique effects of environmental variables, spatial dependence of sampling sites, forest stand age and the combined effect of all groups of variables on species richness of woody plants with different levels of rarity (common, intermediate, rare, very rare)—according to their abundance, habitat specificity and spatial distribution range in the landscape. Analyzing separately common species and those with different levels of rarity uncovered contrasting patterns and correlates of species richness that were not apparent when focusing on all woody plants. In particular, richness of common and intermediate species was influenced mainly by environmental factors, whereas richness of very rare species was affected mostly by the unique effect of spatial dependence of sampling sites, suggesting a main role of environmental filtering and dispersal limitation, respectively. However, common and very rare species also responded inversely to some landscape metrics, revealing contrasting environmental preferences of these groups of species. These contrasting results suggest different underlying mechanisms and the need for very different conservation strategies. Therefore, basic and applied research on tropical forest biodiversity should consider separately species with different levels of rarity, focusing on which factors control variation in each level, and paying special attention to very rare species, generally the most specious and vulnerable to local extinction

Modelización y mapeo estacional del índice de área foliar en un bosque tropical seco usando imágenes de satélite de alta resolución

Abstract The leaf area index (LAI) provides information about the amount of photosynthetic area in relation to the total surface of an ecosystem and it is related to vital processes such as photosynthesis, respiration, and productivity. Thus, it is important to have information about the spatial distribution of LAI at the landscape level. One of the most used methods for estimating LAI from satellite images is to associate it with spectral characteristics of the image and vegetation indices. However, these indices have a strong limitation due to saturation problems, which reduces the possibility of generating accurate LAI maps, particularly in forests with high levels of biomass. Here, we obtained regression models to map LAI in a tropical dry forest of Yucatan, during the rainy and dry seasons from high resolution satellite imagery. We used regression analysis combined with kriging, as this procedure integrates the relationship between LAI and both spectral and texture information of the imagery, as well as the spatial dependence of the observations. LAI values were obtained in the field using hemispheric photographs. The results show that LAI values differ significantly between seasons, with mean values of 3.37 in the rainy season and 2.49 in the dry season. The R2 adj values of the regression analysis were 0.58 and 0.63 for the rainy and dry season respectively. Overall, our results demonstrate that by using texture measures, we are able to obtain accurate estimations of LAI in tropical dry forests with high levels of biomass.Resumen El índice de área foliar (IAF) proporciona información acerca de la cantidad de superficie fotosintética que existe en relación con la superficie total del ecosistema y se relaciona con procesos vitales como la fotosíntesis, la respiración y la productividad. Por lo tanto, es importante contar con información sobre la distribución espacial del IAF a escala de paisaje. El método indirecto más utilizado para la estimación del IAF se basa en imágenes de satélite y consiste en asociarlo con características espectrales e índices de vegetación. Sin embargo, estos índices tienen una fuerte limitación debido a problemas de saturación, lo cual restringe la posibilidad de generar mapas precisos de IAF, particularmente en bosques con altos niveles de biomasa. En el presente trabajo se obtuvieron modelos para mapear el IAF en un bosque tropical seco de Yucatán durante las estaciones de lluvia y estiaje a partir de imágenes de alta resolución, utilizando un procedimiento de regresión combinado con kriging. Este procedimiento integra la relación del IAF, tanto con datos espectrales y de textura de las imágenes, como con la dependencia espacial de los residuales. Se obtuvieron valores de IAF por medio de fotografías hemisféricas con una precisión aceptable y valores medios significativamente diferentes entre la temporada de lluvias (3.37) y la de estiaje (2.49). Los valores de R2 aj de los modelos de regresión múltiple fueron de 0.58 y 0.63 para la temporada de lluvias y estiaje, respectivamente. En general, los resultados demuestran que, al utilizar el análisis de textura, se pueden generar modelos aceptables para la estimación del IAF en bosques tropicales secos con altos niveles de biomasa